Meng Tan Chee, Somani Sandeep, Dhar Pawan
Bioinformatics Institute Singapore.
In Silico Biol. 2004;4(3):293-309.
Mathematical modeling is a powerful approach for understanding the complexity of biological systems. Recently, several successful attempts have been made for simulating complex biological processes like metabolic pathways, gene regulatory networks and cell signaling pathways. The pathway models have not only generated experimentally verifiable hypothesis but have also provided valuable insights into the behavior of complex biological systems. Many recent studies have confirmed the phenotypic variability of organisms to an inherent stochasticity that operates at a basal level of gene expression. Due to this reason, development of novel mathematical representations and simulations algorithms are critical for successful modeling efforts in biological systems. The key is to find a biologically relevant representation for each representation. Although mathematically rigorous and physically consistent, stochastic algorithms are computationally expensive, they have been successfully used to model probabilistic events in the cell. This paper offers an overview of various mathematical and computational approaches for modeling stochastic phenomena in cellular systems.
数学建模是理解生物系统复杂性的一种强大方法。最近，在模拟复杂生物过程（如代谢途径、基因调控网络和细胞信号通路）方面已经取得了一些成功的尝试。这些途径模型不仅产生了可通过实验验证的假设，还为复杂生物系统的行为提供了有价值的见解。最近的许多研究证实，生物体的表型变异性源于基因表达基础水平上存在的内在随机性。因此，开发新的数学表示和模拟算法对于生物系统建模的成功至关重要。关键在于为每种表示找到与生物学相关的表达方式。尽管随机算法在数学上严谨且物理上一致，但计算成本高昂，它们已成功用于模拟细胞中的概率事件。本文概述了用于模拟细胞系统中随机现象的各种数学和计算方法。
